Recently, due to the non-local attention mechanism, transformers [1]} show great success in many visual tasks, such as image classification [2]}, object detection [3]}, [4]}, 3D human pose estimation [5]} and low-level image processing [6]}. Moreover, several methods with visual transformers [7]}, [8]}, [9]} are explored in the MOT field. For example, [7]} proposes a baseline tracker via transformer, which takes advantage of the query-key mechanism and introduces a set of learned object queries into the pipeline to enable detecting new-coming objects. [8]} extends the DETR object detector [3]} and achieves a seamless data association between frames in a new tracking-by-attention paradigm by encoder-decoder and self-attention mechanisms. [9]} leverages graph transformers to efficiently model the spatial and temporal interactions among the objects. However, transformer-based trackers usually require pre-training on large-scale datasets.
